In this section, we investigate the performance of the proposed speaker conditioning method presented in Fig.REF  on WSJ corpus.
In order to conduct the experiments, clean tr-si284 is used in the training phase for all acoustic models. We partitioned tr-si284 into a training set \((90\%)\)  and a held-out cross-validation set \((10\%)\) . ASR performance for different acoustic models are reported in terms of Word-Error-Rate (WER) on clean dev-93, eval-93, and eval-92. Additionally, overlap speech is generated based on dev-93 by selecting random utterances from random speakers and adding them with Signal-to-Interference Ratio (SIR) ranging from 0 to 25dB with increments of 5dB. The baseline acoustic model is ResNet18 with 3400 output senones. The network parameters are updated by the gradients of the cross entropy loss using Stochastic Gradient Descent (SGD) optimizer with a momentum of \(0.9\)  and initial learning rate \(0.01\) . The training process is completed by performing early stopping [1]}. The maximum number of epochs is set to 100, batch size 1024 context-expanded frames; learning rate is decreased by \(50\%\)  if the cv loss improvement is less than \(0.01\)  for 3 successive epochs. The early stopping is performed if no improvement is observed on the cv loss once the learning rate has decayed 6 times. 13-dim MFCC computed over a \(25ms\)  window with \(10ms\)  shift with a 20 frame context (10 frames on each side) is used for training the acoustic model. Consistent with the standard Kaldi recipe for WSJ, we use the trigram language models provided by LDC for WSJ data. In order to minimize the effect of parameter initialization on the acoustic model and final WER, we repeat each experiment three times with different initial parameters.
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